{"id":"W4389941531","doi":"10.1080/07391102.2023.2295382","title":"Machine learning and experimental screening of chromatin regulator signatures and potential drugs in hepatitis B related hepatocellular carcinoma","year":2023,"lang":"en","type":"article","venue":"Journal of Biomolecular Structure and Dynamics","topic":"Immunotherapy and Immune Responses","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Genetics","funders":"","keywords":"Hepatocellular carcinoma; Regulator; Chromatin; Cancer research; Medicine; Chemistry; Computational biology; Pharmacology; Biology; Biochemistry; DNA","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000224434,0.0001561713,0.0003444802,0.0003249137,0.0001096978,0.00001905521,0.00007221619,0.0002309493,0.00002640862],"category_scores_gemma":[0.00003038031,0.000132974,0.00006552318,0.0001690293,0.0002084068,0.00008164308,0.00007556759,0.0003593076,2.476181e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001731226,"about_ca_system_score_gemma":0.00002486138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001182446,"about_ca_topic_score_gemma":0.000004719821,"domain_scores_codex":[0.9989895,0.0002260194,0.000412317,0.0001391078,0.00006099161,0.0001721102],"domain_scores_gemma":[0.9995267,0.00005229973,0.0002861939,0.00007210555,0.000040591,0.00002208331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004822931,0.0000150828,0.03994124,0.00003043822,0.0001590381,0.0001026652,0.001105137,0.00004249336,0.9537137,0.0001309916,0.000003522859,0.004273399],"study_design_scores_gemma":[0.007581251,0.001631647,0.2202045,0.0002894137,0.0001313556,0.003049605,0.002085713,0.02615054,0.7375849,0.0005739916,0.0002506598,0.0004663321],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9561319,0.04337015,0.0001945186,0.0000605075,0.000125929,0.0000828683,0.00002001962,0.000009240088,0.000004906609],"genre_scores_gemma":[0.998561,0.001035411,0.0002392117,0.00001429133,0.00000712862,4.857704e-7,0.00004832292,0.00001721562,0.00007693847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2161288,"threshold_uncertainty_score":0.5422522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002786725331480483,"score_gpt":0.2090040709893476,"score_spread":0.2062173456578671,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}